Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum computing job scheduling are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a scheduler component that can determine a run order of quantum computing jobs based on one or more quantum based run constraints. The computer executable components can further comprise a run queue component that can store the quantum computing jobs based on the run order. In an embodiment, the scheduler component can determine the run order based on availability of one or more qubits comprising a defined level of fidelity.

Abstract: The method includes receiving data characterizing a first segregator code, a first aggregator code and a plurality of configuration parameters associated with a data transmission system. The data transmission system is configured to receive operation data characterizing an operation of an industrial system and transmit a portion of the operation data to a first analytical model. The method also includes modifying the first segregator code and the first aggregator code based on one or more configuration parameters of the plurality of configuration parameters. The method further includes deploying the first segregator code and the first aggregator code in the data transmission system. The first segregator code is configured to provide a first portion of the operation data to the first analytical model via a first data transmission route that includes the first segregator code and the first aggregator code.

Abstract: Disclosed are systems, methods, and articles for determining compatibility of a mobile application and operating system on a mobile device. In some aspects, a method includes receiving one or more data values from a mobile device having a mobile medical software application installed thereon, the data value(s) characterizing a version of the software application, a version of an operating system installed on the mobile device, and one or more attributes of the mobile device; determining whether the mobile medical software application is compatible with the operating system by at least comparing the received data value(s) to one or more test values in a configuration file; and sending a message to the mobile device based on the determining, the message causing the software application to operate in one or more of a normal mode, a safe mode, and a non-operational mode.

Abstract: Systems and methods for generating custom applications for querying a multidimensional database of a target platform include, responsive to receiving a custom application request, an application definition is discovered based on data received from one or more sources. The application definition indicates target outputs of the custom application, influencers for each of the target outputs that correspond to members of one or more first dimensions of the multidimensional database, and granularity definitions relative to second dimensions of the multidimensional database for each influencer. Mutually exclusive groups each including two or more target outputs are generated by applying a weighting algorithm to the application definition, and resource-efficient machine written code is dynamically generated based on the groupings and the results of the weighting algorithm.

Abstract: A compiler-implemented technique for performing a storage allocation is described. Computer code to be converted into machine instructions for execution on an integrated circuit device is received. The integrated circuit device includes a memory having a set of memory locations. Based on the computer code, a set of values that are to be stored on the integrated circuit device are determined. An interference graph that includes the set of values and a set of interferences is constructed. While traversing the interference graph, a set of memory location assignments are generated by assigning the set of values to the set of memory locations in accordance with one or more color selection schemes.

Abstract: A distributed machine learning optimization flow processing engine is proposed. The processing engine takes into account the structure of the programming to assign proper allocation within a distributed computing infrastructure. The processing engine also takes into account availability and loads of the different computing elements within the distributed infrastructure to maximize their utilization according to the software being executed.

Abstract: Disclosed herein are system, method, and computer program product embodiments for conducting taint analysis on inputted data from a user to a process, where based on pre-defined rules, input data may be marked as tainted. In a passive mode, logging or deletion actions may be taken on the tainted data. In an active mode, the process may be interrupted and a user prompt may be displayed each time a taint point is reached.

Abstract: Method and system of automatically creating an instance of a containerized server within an Integrated Development Environment (IDE) is provided. The method includes registering a containerized server in a framework, upon a request from a server developer and creating a docker image from within IDE upon a first request from an end user. The docker image includes metadata corresponding to a project and includes project runtime ID and list of exposed ports. The method includes creating a docker container from within IDE upon a second request from end user, using docker image and based on metadata. The method further includes automatically creating and upon creating the docker container, an instance of the containerized server within the IDE by executing a wrapper class corresponding to the project runtime ID and to the containerized server. The containerized server corresponds to the docker container and wrapper class may be executed by the framework.

Abstract: A code change request prioritization tool that uses weighting values specific to pre-determined factors to concurrently calculate a total weighted prioritization score, is disclosed. The total weighted prioritization score may be calculated using a percentile score for factors as inputs in the calculation. Once a solution is found for a highest priority code change request, similar code defects may be identified so as to provide solutions to all similar defects, simultaneously.

Abstract: Techniques are described for generating a programming score for a user based on source code submissions for a programming task set. In an embodiment, a programming system generates, at random, a programming task set for submission by a user. The programming system receives source codes submitted for the programming task set in a programming session with a client computer system of a user. The programming system executes the source codes to evaluate their accuracy. Based on the execution, the programming system determines that some source codes are evaluated to be accurate thereby solving corresponding programming tasks from the programming task set. Based on such an execution, programming system determines the programming score for the particular user for the programming task set.

Abstract: A method may include obtaining training code and extracting features from the training code. The extracted features of the training code may be mapped to natural language code vectors by a deep neural network. A natural language search query requesting source-code suggestions may be received, and the natural language search query may be mapped to a natural language search vector by the deep neural network. The method may include mapping the natural language search query to the natural language search vector in the same or a similar method as mapping the extracted features of the training code to natural language code vectors, and the natural language search vector may be compared to the natural language code vectors. Source code responsive to the natural language search query may be suggested based on the comparison between the natural language search vector and the natural language code vectors.

Abstract: In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.

Abstract: Disclosed in some examples are memory devices which include electrically programmable elements that specify values for one or more firmware search parameters for use by the bootloader in locating and reading the firmware object. The values of the firmware search parameters may be dynamically selected at manufacturing time by modifying the configuration of the electrically programmable elements by applying or not applying a specified voltage to the electrically programmable elements. In some examples, an electrically programmable element may include: a fuse, an anti-fuse, and/or an e-fuse.

Abstract: Functionality is provided for profiling code by unwinding stacks in frame-pointer omitted executables using C++ exception stack unwinding information. Information is extracted from executable files, and used to optimize stack unwinding operations. In at least one embodiment, the system uses information that has been included for exception handling. Storage of such information can be optimized by exploiting patterns in stack deltas.

Abstract: A sample set of cloud computing resources is dynamically selected for testing a software deployment. Telemetry data associated with the resources and customer support data associated with customers that utilize the resources are obtained. A subset of the customers is selected based on the customer support data, and a candidate subset of the resources is selected based on the selected subset of customers and the telemetry data. Criteria for the selection is based on usage patterns and is configurable. Resources of customers with special support agreements, and customers previously selected, may be excluded from the candidate subset. The sample set of cloud computing resources may be randomly selected from the candidate subset. Software is deployed to the sample set as a B resource group and tested for issues with an A resource group to determine whether to proceed to full deployment, roll back the deployment, and/or retest the software.

Abstract: Methods and systems are described for cross-cloud call detection. The system may include detecting an instruction configured to call a resource. The instruction may include a name indicative of the resource and one or more values. The resource is configured to construct code using the one or more values. The system may determine the resource is incompatible with a service based on at least the name and the one or more values of the instruction. A conversion of source code is halted when the resource is incompatible with the service.

Abstract: Training and/or utilization of a neural decompiler that can be used to generate, from a lower-level compiled representation, a target source code snippet in a target programming language. In some implementations, the lower-level compiled representation is generated by compiling a base source code snippet that is in a base programming language, thereby enabling translation of the base programming language (e.g., C++) to a target programming language (e.g., Python). In some of those implementations, output(s) from the neural decompiler indicate canonical representation(s) of variables. Technique(s) can be used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated using the neural decompiler, and a subset (e.g., one) is selected based on evaluation(s).

Abstract: A method for computing includes providing software source code defining a processing pipeline including multiple, sequential stages of parallel computations, in which a plurality of processors apply a computational task to data read from a buffer. A static code analysis is applied to the software source code so as to break the computational task into multiple, independent work units, and to define an index space in which the work units are identified by respective indexes. Based on the static code analysis, mapping parameters that define a mapping between the index space and addresses in the buffer are computed, indicating by the mapping the respective ranges of the data to which the work units are to be applied. The source code is compiled so that the processors execute the work units identified by the respective indexes while accessing the data in the buffer in accordance with the mapping.

Abstract: Disclosed is a computer program stored in a computer readable storage medium, the computer program including commands which cause a processor of a server to execute operations below, the operations including: if a first program written in a source language is converted to a second program written in a target language, generating block information for the first program by analyzing the first program; inserting an annotation into the second program, wherein the annotation includes information on at least one source line, which is included in the first program, associated with at least one target line included in the second program; generating meta information associated with runtime verification based on the block information and the annotation.

Abstract: Analyzing or debugging applications is provided. The system identifies an action for an application provided by a developer. The system determines a first classification score based on historical execution of the action. The system generates a machine generated action for the application based on metadata associated with the application. The system determines a second classification score based on a comparison of the action with the machine generated action. The system selects, via a matching program, a second application that matches the application. The system determines a third classification score based on a comparison of an action approved for the second application with the action provided by the application developer. The system updates a delivery control parameter based on the first classification score, the second classification score and the third classification score. The system controls delivery of the application based on the delivery control parameter.